AI/ML Engineer

Abbott

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Alameda, CA
Salary: $100,000–$200,000 / yr
Employment: Full-time
Posted: 15 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $211k

This role $150k

$80k $283k

below market most similar roles pay here above market

This role pays less than 92% of similar roles. Most pay $175,750–$247,115 — the blue band above. At the midpoint, this role pays about $150k versus about $211k for comparable roles.

Based on 240 similar postings.

Employer

About Abbott

Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices

Abbott currently has 193 open roles on FindRole.

Listed pay typically runs $99,300–$198,700 across 123 roles with salary data.

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At a glance

TL;DR · AI/ML Engineer

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JOB TITLE: AI/ML Engineer The AI/ML Engineer joins the Lingo team to develop a health platform integrating continuous biosensor data with real-time analytics. You will be responsible for bringing machine learning models to mobile phones and wearables to provide real-time insights without network connectivity. Your daily work involves owning on-device inference, model optimization, and designing the logic for cloud and local execution splits. You will convert and deploy models using Core ML, TensorFlow Lite, or ONNX Runtime while applying quantization, pruning, and distillation to meet strict latency and battery targets. The role requires proficiency in Python and Swift, Kotlin, or C++. You will solve technical challenges regarding clinical accuracy, offline behavior, and synchronization within a regulated medical device and software environment.

Skills

Python Swift Kotlin C++ Core ML TensorFlow Lite ONNX Runtime Machine Learning Model Optimization Quantization Pruning Distillation iOS Android Edge Inference Federated Learning Bluetooth IEC 62304

What you'll do

Convert, optimize, and deploy machine learning models to iOS, Android, and wearable devices.
Apply quantization, pruning, and distillation to meet latency, memory, and battery targets.
Design the logic for on-device and cloud split, including offline behavior and result synchronization.
Build a safe, versioned process for updating models on devices in partnership with the ML platform team.
Validate that on-device model outputs match reference models across different device types and OS versions.
Collect on-device performance and quality metrics while ensuring user privacy.
Integrate models into the mobile app and sensor experience in partnership with mobile engineers.
Contribute to design reviews, verification activities, and technical documentation for regulatory compliance.

What we're looking for

Bachelor’s or Master’s degree in Computer Science or a related field.
6+ years of software or ML engineering experience.
3+ years of experience deploying ML models on mobile or embedded devices.
Hands-on experience with Core ML, TensorFlow Lite, ONNX Runtime, or equivalent edge inference frameworks.
Proven experience with model optimization techniques such as quantization and pruning.
Proficiency in Python plus at least one of Swift, Kotlin, or C++.
Understanding of mobile performance constraints including battery, memory, latency, and background execution.
Experience in a regulated industry, medical devices, wearable devices, or running small language models on device (preferred).